Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Graph neural networks (GNNs) have been used to tackle the few-shot learning (FSL)
problem and shown great potentials under the transductive setting. However under the …

Accurately recommending the next point of interest (POI) has become a fundamental
problem with the rapid growth of location-based social networks. However, sparse …

Continual learning agents experience a stream of (related) tasks. The main challenge is that
the agent must not forget previous tasks and also adapt to novel tasks in the stream. We are …

Continual learning studies agents that learn from streams of tasks without forgetting previous
ones while adapting to new ones. Two recent continual-learning scenarios have opened …

Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source
domain to a label-scarce target domain while addressing disturbances from irrelevant target …

Domain adaptation techniques, which focus on adapting models between distributionally
different domains, are rarely explored in the video recognition area due to the significant …

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information to
recognize seen and unseen samples, where unseen classes are not observable during …

Over the past decade, deep neural networks have demonstrated significant success using
the training scheme that involves mini-batch stochastic gradient descent on extensive …